Abstract. In this study, the spatio-temporal and seasonal distributions of EOS/Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical depth (AOD) over East Asia were analyzed in conjunction with US EPA Models-3/CMAQ v4.3 modeling. In this study, two MODIS AOD products (τ MODIS : τ M−BAER and τ NASA ) retrieved through a modified Bremen Aerosol Retrieval (M-BAER) algorithm and NASA collection 5 (C005) algorithm were compared with the AOD (τ CMAQ ) that was calculated from the US EPA Models-3/CMAQ model simulations. In general, the CMAQ-predicted AOD values captured the spatial and temporal variations of the two MODIS AOD products over East Asia reasonably well. Since τ MODIS cannot provide information on the aerosol chemical composition in the atmosphere, different aerosol formation characteristics in different regions and different seasons in East Asia cannot be described or identified by τ MODIS itself. Therefore, the seasonally and regionally varying aerosol formation and distribution characteristics were investigated by the US EPA Models-3/CMAQ v4.3 model simulations. The contribution of each particulate chemical species to τ MODIS and τ CMAQ showed strong spatial, temporal and seasonal variCorrespondence to: C. H. Song (chsong@gist.ac.kr) ations. For example, during the summer episode, τ MODIS and τ CMAQ were mainly raised due to high concentrations of (NH 4 ) 2 SO 4 over Chinese urban and industrial centers and secondary organic aerosols (SOAs) over the southern parts of China, whereas during the late fall and winter episodes, τ MODIS and τ CMAQ were higher due largely to high levels of NH 4 NO 3 formed over the urban and industrial centers, as well as in areas with high NH 3 emissions. τ CMAQ was in general larger than τ MODIS during the year, except for spring. The high biases (τ CMAQ >τ MODIS ) may be due to the excessive formation of both (NH 4 ) 2 SO 4 (summer episode) and NH 4 NO 3 (fall and winter episodes) over China, possibly from the use of overestimated values for NH 3 emissions in the CMAQ modeling. According to CMAQ modeling, particulate NH 4 NO 3 made a 14% (summer) to 54% (winter) contribution to σ ext and τ CMAQ . Therefore, the importance of NH 4 NO 3 in estimating τ should not be ignored, particularly in studies of the East Asian air quality. In addition, the accuracy of τ M−BAER and τ NASA was evaluated by a comparison with the AOD (τ AERONET ) from the AERONET sites in East Asia. Both τ M−BAER and τ NASA showed a strong correlation with τ AERONET around the 1:1 line (R=0.79), indicating promising potential for the application of both the M-BAER and NASA aerosol retrieval algorithms to satellite-based air quality monitoring studies in East Asia.

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This paper concerns contour-based algorithms for generating a 3D CAD model from medical images. The 3D model generated by contour-based algorithms can be used to generate CAM data for fabrication where the accuracy is of most concern. The overall procedure includes: (1) contour data extraction from medical images, (2) smoothing of the extracted contours, and (3) creation of a surface model from contours. For this, various methods should be applied to generate a highquality surface model. The main contribution of this paper is to propose a new contour smoothing method, called bi-directional smoothing. The basic idea behind the proposed bi-directional smoothing method is to refine contours along both (u, v) parametric directions. Compared to conventional smoothing methods, the recontouring that comprises the first part of the method can prevent the shape of a contour from shrinking with a large number of iterations. Along with recontouring, a vertical connectivity estimation and a vertical smoothing method are also proposed. The overall procedure for this approach is demonstrated with an application example using CT images of a femur.

Extracting exact features from noisy point data is an important problem, in practice, for the application of reverse engineering. Several feature extraction methods have been used to handle noisy point data, such as the "angular" method and the "chordal" method. They work well for most cases, but the generation of extra features cannot be avoided for some cases. A new feature extraction method that deals with noisy scanned point data is proposed in this paper. We call it the iterative angular feature extraction (IAFE) method, since it extends the concept of the angular method. The IAFE method first distinguishes the feature regions from point clouds, then the iterative algorithm is applied to refine each feature region into ultimate feature points. A "noise dilution" concept is used to reduce the noise effect. A "multiple point" algorithm, an "angle variation" algorithm and an "iterations for convergence" algorithm are developed to implement the noise dilution concept. The IAFE(I) method for planar models and the IAFE(II) method for curved models are designed. The IAFE method demonstrated its usefulness in dealing with noisy point data.

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